mirror of https://github.com/vladmandic/human
59 lines
2.8 KiB
TypeScript
59 lines
2.8 KiB
TypeScript
import { log } from '../log';
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import * as tf from '../../dist/tfjs.esm.js';
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import * as blazeface from './blazeface';
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import * as facepipeline from './facepipeline';
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import * as coords from './coords';
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export class MediaPipeFaceMesh {
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facePipeline: any;
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config: any;
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constructor(blazeFace, blazeMeshModel, irisModel, config) {
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this.facePipeline = new facepipeline.Pipeline(blazeFace, blazeMeshModel, irisModel, config);
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this.config = config;
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}
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async estimateFaces(input, config) {
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const predictions = await this.facePipeline.predict(input, config);
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const results: Array<{}> = [];
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for (const prediction of (predictions || [])) {
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if (prediction.isDisposedInternal) continue; // guard against disposed tensors on long running operations such as pause in middle of processing
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const mesh = prediction.coords ? prediction.coords.arraySync() : null;
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const meshRaw = prediction.rawCoords;
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const annotations = {};
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if (mesh && mesh.length > 0) {
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for (const key of Object.keys(coords.MESH_ANNOTATIONS)) {
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annotations[key] = coords.MESH_ANNOTATIONS[key].map((index) => mesh[index]);
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}
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}
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const boxRaw = (config.face.mesh.returnRawData && prediction.box) ? { topLeft: prediction.box.startPoint, bottomRight: prediction.box.endPoint } : null;
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const box = prediction.box ? [
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Math.max(0, prediction.box.startPoint[0]),
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Math.max(0, prediction.box.startPoint[1]),
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Math.min(input.shape[2], prediction.box.endPoint[0]) - prediction.box.startPoint[0],
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Math.min(input.shape[1], prediction.box.endPoint[1]) - prediction.box.startPoint[1],
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] : 0;
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results.push({ confidence: prediction.confidence || 0, box, mesh, boxRaw, meshRaw, annotations, image: prediction.image ? tf.clone(prediction.image) : null });
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if (prediction.coords) prediction.coords.dispose();
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if (prediction.image) prediction.image.dispose();
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}
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return results;
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}
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}
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let faceModels = [null, null, null];
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export async function load(config) {
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// @ts-ignore
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faceModels = await Promise.all([
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(!faceModels[0] && config.face.enabled) ? blazeface.load(config) : null,
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(!faceModels[1] && config.face.mesh.enabled) ? tf.loadGraphModel(config.face.mesh.modelPath, { fromTFHub: config.face.mesh.modelPath.includes('tfhub.dev') }) : null,
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(!faceModels[2] && config.face.iris.enabled) ? tf.loadGraphModel(config.face.iris.modelPath, { fromTFHub: config.face.iris.modelPath.includes('tfhub.dev') }) : null,
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]);
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const faceMesh = new MediaPipeFaceMesh(faceModels[0], faceModels[1], faceModels[2], config);
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if (config.face.mesh.enabled) log(`load model: ${config.face.mesh.modelPath.match(/\/(.*)\./)[1]}`);
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if (config.face.iris.enabled) log(`load model: ${config.face.iris.modelPath.match(/\/(.*)\./)[1]}`);
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return faceMesh;
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}
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exports.triangulation = coords.TRI468;
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